yeastvision 0.1.16

Deep learning-enabled image analysis of the yeast full life cycle

YeastVision

Installation

Local installation (< 2 minutes)

System requirements

This package supports Linux, Windows and Mac OS. Mac Os should be later than Yosemite. This system has been heavily tested on Linux and Mac OS machines, and less thoroughly on Windows.

Instructions

If you have an older yeastvision environment you should remove it with conda env remove -n yeastvision before creating a new one.

Yeastvision is ready to go for cpu-usage as soon as it downloaded. GPU-usage requires some additional steps after download. To download:

1. Install an Anaconda distribution of Python. Note you might need to use an anaconda prompt if you did not add anaconda to the path.
2. Open an anaconda prompt/command prompt
3. Create a new environment with conda create --name yeastvision python=3.10.0.
4. Activate this new environment by running conda activate yeastvision
5. Run python -m pip install yeastvision to download our package plus all dependencies
6. Download the weights online.
7. Run install-weights in the same directory as the yeastvision_weights.zip file

You should upgrade yeastvision (package here) periodically as it is still in development. To do so, run the following in the environment:

python -m pip install yeastvision --upgrade

Using YeastVision with Nvidia GPU

Again, enusre your yeastvision conda environment is active for the following commands.

To use your NVIDIA GPU with python, you will first need to install the NVIDIA driver for your GPU, check out this website to download it. Ensure it is downloaded and your GPU is detected by running nvidia-smi in the terminal.

Yeastvision relies on two machine-learning frameworks: tensorflow and pytorch. We will need to configure both of these packages for gpu usage

PyTorch

First, we need to remove the CPU version of torch:

pip uninstall torch

And the cpu version of torchvision:

pip uninstall torchvision

Now install torch and torchvision for CUDA version 11.3 (Ensure that your nvidia drivers are up to date for version 11.3 by running nvidia-smi and check that a version >=11.3 is displayed in the top right corner of the output table).

conda install pytorch==1.12.0 torchvision==0.13.0 cudatoolkit=11.3 -c pytorch

After install you can check conda list for pytorch, and its version info should have cuXX.X, not cpu.

Tensorflow

All we need to do here is install the cuDNN package for tensorflow gpu usage

conda install cudnn=8.1.0

Common Installation Problems

You may receive the following error upon upgrading torch and torchvision:

AttributeError: partially initialized module 'charset_normalizer' has no attribute 'md__mypyc' (most likely due to a circular import)

This is solved by upgrading the charselt_normalizer package with the following command: pip install --force-reinstall charset-normalizer==3.1.0

Report any other installation errors.

Run yeastvision locally

The quickest way to start is to open the GUI from a command line terminal. Activate the correct conda environment, then run:

yeastvision

To get started, drop an image or directory of images into the GUI.

Masks can be loaded by dropping them into the top half of the screen.